Drug side effects have become paramount concerns in drug safety research,ranking as the fourth leading cause of mortality following cardiovascular diseases,cancer,and infectious diseases.Simultaneously,the widespread ...Drug side effects have become paramount concerns in drug safety research,ranking as the fourth leading cause of mortality following cardiovascular diseases,cancer,and infectious diseases.Simultaneously,the widespread use of multiple prescription and over-the-counter medications by many patients in their daily lives has heightened the occurrence of side effects resulting from Drug-Drug Interactions(DDIs).Traditionally,assessments of drug side effects relied on resource-intensive and time-consuming laboratory experiments.However,recent advancements in bioinformatics and the rapid evolution of artificial intelligence technology have led to the accumulation of extensive biomedical data.Based on this foundation,researchers have developed diverse machine learning methods for discovering and detecting drug side effects.This paper provides a comprehensive overview of recent advancements in predicting drug side effects,encompassing the entire spectrum from biological data acquisition to the development of sophisticated machine learning models.The review commences by elucidating widely recognized datasets and Web servers relevant to the field of drug side effect prediction.Subsequently,The study delves into machine learning methods customized for binary,multi-class,and multi-label classification tasks associated with drug side effects.These methods are applied to a variety of representative computational models designed for identifying side effects induced by single drugs and DDIs.Finally,the review outlines the challenges encountered in predicting drug side effects using machine learning approaches and concludes by illuminating important future research directions in this dynamic field.展开更多
Background:Diminished sensitivity towards chemotherapy remains the major impediment to the clinical treatment of bladder cancer.However,the critical elements in control of chemotherapy resistance remain obscure.Method...Background:Diminished sensitivity towards chemotherapy remains the major impediment to the clinical treatment of bladder cancer.However,the critical elements in control of chemotherapy resistance remain obscure.Methods:We adopted improved collagen gels and performed cytotoxicity analysis of doxorubicin(DOX)and mitomycin C(MMC)of bladder cancer cells in a 3D culture system.We then detected the expression of multidrug resistant gene ABCB1,dormancy-associated functional protein chicken ovalbumin upstream-transcription factor 1(COUPTF1),cell proliferation marker Ki-67,and cellular senescence marker senescence-associatedβ-galactosidase(SA-β-Gal)in these cells.We further tested the effects of integrin blockade or protein kinase B(AKT)inhibitor on the senescent state of bladder cancer.Also,we examined the tumor growth and survival time of bladder cancer mouse models given the combination treatment of chemotherapeutic agents and integrinα2β1 ligand peptide TFA(TFA).Results:Collagen gels played a repressive role in bladder cancer cell apoptosis induced by DOX and MMC.In mechanism,collagen activated the integrinβ1/AKT cascade to drive bladder cancer cells into a premature senescence state via the p21/p53 pathway,thus attenuating chemotherapy-induced apoptosis.In addition,TFA had the ability to mediate the switch from senescence to apoptosis of bladder cancer cells in xenograft mice.Meanwhile,TFA combined with chemotherapeutic drugs produced a substantial suppression of tumor growth as well as an extension of survival time in vivo.Conclusions:Based on our finding that integrinβ1/AKT acted primarily to impart premature senescence to bladder cancer cells cultured in collagen gel,we suggest that integrinβ1 might be a feasible target for bladder cancer eradication.展开更多
基金supported in part by the National Natural Science Foundation of China(Nos.62072473,U22A2041)the National Funded Postdoctoral Program of China(No.G2C20233162)the Natural Science Foundation of Hunan Province(No.2022JJ30750).
文摘Drug side effects have become paramount concerns in drug safety research,ranking as the fourth leading cause of mortality following cardiovascular diseases,cancer,and infectious diseases.Simultaneously,the widespread use of multiple prescription and over-the-counter medications by many patients in their daily lives has heightened the occurrence of side effects resulting from Drug-Drug Interactions(DDIs).Traditionally,assessments of drug side effects relied on resource-intensive and time-consuming laboratory experiments.However,recent advancements in bioinformatics and the rapid evolution of artificial intelligence technology have led to the accumulation of extensive biomedical data.Based on this foundation,researchers have developed diverse machine learning methods for discovering and detecting drug side effects.This paper provides a comprehensive overview of recent advancements in predicting drug side effects,encompassing the entire spectrum from biological data acquisition to the development of sophisticated machine learning models.The review commences by elucidating widely recognized datasets and Web servers relevant to the field of drug side effect prediction.Subsequently,The study delves into machine learning methods customized for binary,multi-class,and multi-label classification tasks associated with drug side effects.These methods are applied to a variety of representative computational models designed for identifying side effects induced by single drugs and DDIs.Finally,the review outlines the challenges encountered in predicting drug side effects using machine learning approaches and concludes by illuminating important future research directions in this dynamic field.
基金supported by the National Natural Science Foundation of China(Grants No.81902578,81974098,and 8197032158)the National Key Research and Development Program of China(Grants No.2017YFC0908003 and 2017YFC0908004)+2 种基金the Project of Health Commission of Sichuan Province(Grant No.20PJ062)Post-doctoral Science Research Foundation of Sichuan University(Grant No.2020SCU12041)Post-doctor Research Project,West China Hospital,Sichuan University(Grant No.2018HXBH084).
文摘Background:Diminished sensitivity towards chemotherapy remains the major impediment to the clinical treatment of bladder cancer.However,the critical elements in control of chemotherapy resistance remain obscure.Methods:We adopted improved collagen gels and performed cytotoxicity analysis of doxorubicin(DOX)and mitomycin C(MMC)of bladder cancer cells in a 3D culture system.We then detected the expression of multidrug resistant gene ABCB1,dormancy-associated functional protein chicken ovalbumin upstream-transcription factor 1(COUPTF1),cell proliferation marker Ki-67,and cellular senescence marker senescence-associatedβ-galactosidase(SA-β-Gal)in these cells.We further tested the effects of integrin blockade or protein kinase B(AKT)inhibitor on the senescent state of bladder cancer.Also,we examined the tumor growth and survival time of bladder cancer mouse models given the combination treatment of chemotherapeutic agents and integrinα2β1 ligand peptide TFA(TFA).Results:Collagen gels played a repressive role in bladder cancer cell apoptosis induced by DOX and MMC.In mechanism,collagen activated the integrinβ1/AKT cascade to drive bladder cancer cells into a premature senescence state via the p21/p53 pathway,thus attenuating chemotherapy-induced apoptosis.In addition,TFA had the ability to mediate the switch from senescence to apoptosis of bladder cancer cells in xenograft mice.Meanwhile,TFA combined with chemotherapeutic drugs produced a substantial suppression of tumor growth as well as an extension of survival time in vivo.Conclusions:Based on our finding that integrinβ1/AKT acted primarily to impart premature senescence to bladder cancer cells cultured in collagen gel,we suggest that integrinβ1 might be a feasible target for bladder cancer eradication.
